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<table width="100%" summary="page for titanic"><tr><td>titanic</td><td align="right">R Documentation</td></tr></table>

<h2>
titanic
</h2>

<h3>Description</h3>



<p>Passenger survival data from 1912 Titanic shipping accident. 
</p>


<h3>Usage</h3>

<pre>data(titanic)</pre>


<h3>Format</h3>


<p>A data frame with 1316 observations on the following 4 variables.
</p>

<dl>
<dt><code>survived</code></dt><dd><p>1=survived; 0=died</p>
</dd>
<dt><code>age</code></dt><dd><p>1=adult; 0=child</p>
</dd>
<dt><code>sex</code></dt><dd><p>1=Male; 0=female</p>
</dd>
<dt><code>class</code></dt><dd><p>ticket class 1= 1st class; 2= second class; 3= third class</p>
</dd>
</dl>



<h3>Details</h3>



<p>titanic is saved as a data frame.
Used to assess risk ratio; not stardard count model; good binary response model.  
</p>


<h3>Source</h3>



<p>Found in many other texts
</p>


<h3>References</h3>



<p>Hilbe, Joseph M (2007, 2011), Negative Binomial Regression, Cambridge University Press
Hilbe, Joseph M (2009), Logistic Regression Models, Chapman &amp; Hall/CRC
</p>


<h3>Examples</h3>

<pre>
data(titanic)
glmlr &lt;- glm(survived ~ age + sex + factor(class), family=binomial, data=titanic)
summary(glmlr)
exp(coef(glmlr))
</pre>


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